An image protection method suitable for medical internet of things

Abstract

The application discloses an image protection method suitable for a medical Internet of Things, and comprises the following steps: setting a key; acquiring initial parameters required by one-dimensional chaotic mapping by using the key, including parameters and initial values of a chaotic mapping equation; respectively performing scrambling processing on two clear images collected on an Internet of Things device to obtain two scrambled images; merging the two scrambled images to obtain a merged image; performing XOR diffusion on the merged image in combination with the acquired initial parameters required by one-dimensional chaotic mapping to obtain an XOR diffused image; splitting the XOR diffused image to obtain two split images; performing re-scrambling processing on the two split images to obtain an encrypted image; when it is required to view the two clear images collected on the Internet of Things device, performing reverse processing on the encrypted image to finally obtain the two clear images. The application has good encryption effect and can greatly improve the security in a double-image transmission process.

Description

Technical Field

[0001] The present invention relates to the technical field of information security, and in particular to an image protection method applicable to the medical Internet of Things. Background Art

[0002] In recent years, smart devices and related technologies, such as the Internet of Things (IoT) and the Internet of Medical Things (IoMT), have proliferated. However, the limited processing power and storage capacity of smart devices make them vulnerable to cyberattacks, rendering traditional security and cryptographic technologies inadequate. Unlike traditional text messages, image data has a large data capacity, which makes traditional encryption algorithms such as AES and DES face performance bottlenecks when processing large amounts of image data. Therefore, the introduction of chaotic systems has become an effective solution. Chaotic systems are nonlinear dynamic systems known for their sensitivity to initial values, ergodicity, and aperiodicity, making them well-suited for efficient and secure IoT data encryption. Hyperchaotic systems, in particular, have become a research hotspot in the field of digital IoT data encryption due to their enhanced randomness and unpredictability.

[0003] Furthermore, in the context of the Internet of Things (IoT), real-time data processing and transmission place higher demands on security. Traditional IoT image data encryption technologies often struggle to meet these requirements due to their high resource consumption and processing latency. Furthermore, images in the IoT often appear as two separate images. For example, in the medical IoT, doctors may analyze images of a patient's condition taken from two different angles to better assess their condition. In these cases, these images are transmitted through IoT devices within the hospital, but the security of traditional scrambling and diffusion frameworks is often insufficient. Summary of the Invention

[0004] The purpose of the present invention is to overcome the shortcomings of the prior art and provide an image protection method suitable for medical Internet of Things.

[0005] To achieve the above objectives, the technical solutions provided by the present invention are:

[0006] An image protection method applicable to medical Internet of Things, comprising:

[0007] Set the key;

[0008] The key is used to obtain the initial parameters required for the one-dimensional chaotic mapping, including the parameters and initial values ​​of the chaotic mapping equation;

[0009] Perform scrambling on the two plaintext images collected from the IoT device to obtain two scrambled images.

[0010] Merge the two scrambled images to obtain a merged image;

[0011] XOR diffusion is performed on the combined image according to the initial parameters required by the obtained one-dimensional chaotic mapping, to obtain an XOR diffusion image;

[0012] The XOR diffusion image is split to obtain two split images;

[0013] The two split images are subjected to re-disordering processing to obtain an encrypted image;

[0014] When it is necessary to view the two plaintext images collected on the Internet of Things device, the encrypted image is subjected to reverse processing, and finally the two plaintext images are obtained.

[0015] Further, the two plaintext images collected on the Internet of Things device are subjected to disorder processing by the positive DNA scan disorder algorithm, and the disorder process includes:

[0016] A1, input the plaintext image img, obtain the length m and width n of the plaintext image img, initialize the loop parameter i to 1, and initialize the loop parameter j to 1;

[0017] A2, check i, if i is m / 2+1, execute step A8, otherwise execute step A2;

[0018] A3, check j, if j is 2n+1, set the value of i to increment 1, set j to 1, and return to step A2, otherwise go to step A4;

[0019] A4, according to the value of j, select the operation to be executed, the vertical coordinate of the pixel value:

[0020]

[0021] A5, according to the value of j, select the operation to be executed, the horizontal coordinate of the pixel value:

[0022]

[0023] A6, check the values of x and y, if x≤m&&y≤n&&x>0&&y>0 are satisfied at the same time, execute the Line(num)=img(x,y) operation, num=num+1, num represents the position ordinal of the matrix Line to be filled;

[0024] A7, set the value of i to increment 1, set the value of j to increment 1, and return to step A2;

[0025] A8, use the Reshpe formula to change the scanned matrix Line with a length of 1 and a width of m*n into a matrix Line with a length of m and a width of n.

[0026] Furthermore, the two scrambled images are merged, including:

[0027] Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where the size of img1 is m1×n1, the size of img2 is m2×n2, the size of P1 is 1×(m1×n1), and the size of P2 is 1×(m2×n2);

[0028] Connect P1 and P2 end to end to form a one-dimensional vector P of 1×(m1×n1+m2×n2).

[0029] Furthermore, the combined image is subjected to XOR diffusion, including:

[0030] Assume that the size of the one-dimensional vector P obtained by merging the two scrambled images is 1×U, and its one-dimensional vector length L=1×U;

[0031] Use the chaotic system to generate a one-dimensional chaotic sequence key_stream with a length of L, where 1 <i<N0+L且i为int型变量,N0为设定值;

[0032] Perform bit-by-bit XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_strean to obtain the diffused sequence diffusion_img;

[0033] Convert the ciphertext one-dimensional vector diffusion_img back into an image of size U.

[0034] Furthermore, the process of generating the one-dimensional chaotic sequence key_stream includes:

[0035] B1. Initialize variables: x(0)=x0, i=1; x0 is the initial value obtained using the key, specifically the starting point of chaos generation;

[0036] B2. Iterate the one-dimensional chaotic map N0+L times to obtain an array sequence x of size 1*(N0+L) consisting of x(1)-x(N0+L). The specific steps of each iteration are divided into b1 and b2:

[0037] b1: Update the value of x(i) using the chaotic mapping formula:

[0038]

[0039] Among them, b is the control parameter;

[0040] b2: i = i + 1;

[0041] B3. Take x(N0+1)-x(N0+L) as the new x array sequence and discard x(1)-x(N0);

[0042] B4. Perform the following processing on the x sequence to convert it to the interval [0, 255] to obtain a one-dimensional chaotic sequence key_stream with a length of L;

[0043] key_stream(i)=mod(floor(x(i)×10^12),256); i=1:L

[0044] Among them, mod is the remainder operation, and floor is the rounding down operation.

[0045] Furthermore, the image after XOR diffusion is split, including:

[0046] Convert the XOR-diffused image into a one-dimensional vector P3 of size 1×(m1×n1+m2×n2);

[0047] Split the one-dimensional vector P3 into a one-dimensional vector P4 of size 1×(m1×n1) and a one-dimensional vector P5 of size 1×(m2×n2);

[0048] Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P4 of size 1×

[0049] The one-dimensional vector P5 of (m2×n2) becomes an image img3 of size m1×n1 and an image img4 of size m2×n2, respectively.

[0050] Furthermore, the two split images are re-scrambled using the anti-DNA scanning scrambling algorithm. The re-scrambling process includes:

[0051] C1. Input image Img, obtain the length M and width N of image Img, initialize loop parameter I to 1, and initialize loop parameter J to 1;

[0052] C2. Check I. If I is M / 2+1, go to step C8; otherwise, go to step C2.

[0053] C3. Check J. If J is 2N+1, increment the value of I by 1, set J to 1, and return to step C2. Otherwise, go to step C4.

[0054] C4. Select the operation to be performed based on the value of J, the vertical coordinate of the pixel value:

[0055]

[0056] C5. Select the operation to be performed based on the value of J. The horizontal coordinate of the pixel value:

[0057]

[0058] C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0, execute line(Num)=Img(X,1+NY) operation, where Num=Num+1, where Num represents the position number of the matrix line to be filled with pixels.

[0059] C7. Set the value of I to increment by 1, the value of J to increment by 1, and return to step C2;

[0060] C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

[0061] Furthermore, the encrypted image is reverse processed, and the process includes:

[0062] The two encrypted images are inversely scrambled using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images.

[0063] Merge the two inverse scrambled images to obtain a merged image;

[0064] Combined with the initial parameters required for the obtained one-dimensional chaotic map, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion;

[0065] Split the image after inverse XOR diffusion to obtain two split images;

[0066] When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are reverse-scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

[0067] Compared with the existing technology, the principles and advantages of this technical solution are as follows:

[0068] 1. The dual-image scrambling-merging-diffusion-splitting-re-scrambling encryption method suitable for the Internet of Things has good encryption effect and can greatly improve the security of dual-image transmission.

[0069] 2. The plaintext image is scrambled using the positive DNA scanning scrambling algorithm, and the split image is re-scrambled using the anti-DNA scanning scrambling algorithm. The combination of the two can significantly improve the scrambling effect and effectively resist attacks based on traditional scan line scrambling methods.

[0070] 3. Adoption A chaotic mapping is performed, which is sensitive enough to the key, difficult to crack, and can generate a sufficiently random sequence. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the services required in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative effort.

[0072] Figure 1 A principle flowchart of the image protection method suitable for medical Internet of Things according to the present application;

[0073] Figure 2 A comparison chart of before and after encryption of two images using the method according to the present application;

[0074] Figure 3 A principle schematic diagram of the positive DNA scan scrambling algorithm;

[0075] Figure 4 A principle schematic diagram of the reverse DNA scan scrambling algorithm. DETAILED DESCRIPTION

[0076] The present application will be further described below in combination with specific embodiments:

[0077] As shown in the drawings, the image protection method suitable for medical Internet of Things according to the present embodiment comprises the following steps: Figure 1

[0078] S1, setting a key;

[0079] S2, using the key to obtain initial parameters required for one-dimensional chaotic mapping, including parameters and initial values of chaotic mapping equation;

[0080] S3, scrambling the two plaintext images collected on the Internet of Things device through the positive DNA scan scrambling algorithm respectively to obtain two scrambled images;

[0081] The scrambling process of this step is as follows:

[0082] A1, inputting a plaintext image img to obtain the length m and width n of the plaintext image img, initializing a loop parameter i as 1, and initializing a loop parameter j as 1;

[0083] A2, checking i, if i is m / 2+1, executing step A8, otherwise executing step A2;

[0084] A3, checking j, if j is 2n+1, setting the value of i to increment by 1, setting j as 1, and returning to step A2, otherwise executing step A4;

[0085] A4, selecting the operation to be executed according to the value of j, the vertical coordinate of the pixel value: ​

[0086]

[0087] A5. Select the operation to be performed based on the value of j. The horizontal coordinate of the pixel value is:

[0088]

[0089] A6. Check the values ​​of x and y. If x≤m &&y≤n &&x>0 &&y>0, execute the Line(num)=img(x,y) operation, where num=num+1, where num represents the position number of the matrix Line of the pixel to be filled.

[0090] A7. Increment the value of i and j by 1, and return to step A2.

[0091] A8. Use the Reshpe formula to convert the scanned matrix Line with a length of 1 and a width of m*n into a matrix Line with a length of m and a width of n.

[0092] S4, merging the two scrambled images to obtain a merged image;

[0093] The merging process of this step is as follows:

[0094] Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where the size of img1 is m1×n1, the size of img2 is m2×n2, the size of P1 is 1×(m1×n1), and the size of P2 is 1×(m2×n2);

[0095] Connect P1 and P2 end to end to form a one-dimensional vector P of 1×(m1×n1+m2×n2).

[0096] S5. Combining the obtained initial parameters required for the one-dimensional chaotic mapping, performing XOR diffusion on the merged image to obtain an XOR-diffused image;

[0097] The specific process of XOR diffusion on the merged image is as follows:

[0098] Assume that the size of the one-dimensional vector P obtained by merging the two scrambled images is 1×U, and its one-dimensional vector length L=1×U;

[0099] Use the chaotic system to generate a one-dimensional chaotic sequence key_stream with a length of L, where 1 <i<N0+L且i为int型变量,N0为设定值;

[0100] Perform bit-by-bit XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_stream to obtain the diffused sequence diffusion_img;

[0101] The ciphertext one-dimensional vector diffusion_img is converted into an image with a size of U again.

[0102] In the above, the process of generating the one-dimensional chaotic sequence key_strean includes:

[0103] B1, initialize variables: x(0) = x0, i = 1; x0 is an initial value obtained by using a key, specifically the starting point of chaotic generation;

[0104] B2, the one-dimensional chaotic mapping iteration loop is performed N0+L times, and an array sequence x with a size of 1*(N0+L) is obtained, which is composed of x(1)-x(N0+L); the specific steps of each time are divided into b1 and b2:

[0105] b1: update the value of x(i) using the chaotic mapping formula:

[0106]

[0107] Wherein, b is a control parameter;

[0108] b2: i = i + 1;

[0109] B3, intercept x(N0+1)-x(B0+L) as a new x array sequence, and discard x(1)-x(B0);

[0110] B4, process the x sequence to convert it to the [0, 255] interval to obtain a one-dimensional chaotic sequence key_stream with a length of L;

[0111] key_stream(i) = mod(floor(x(i)*10^12), 256); i = 1:L

[0112] Wherein, mod is the modulo operation, and floor is the floor operation.

[0113] S6, split the image after the XOR diffusion to obtain two split images;

[0114] The process of splitting the image after the XOR diffusion includes:

[0115] The image after the XOR diffusion is converted into a one-dimensional vector P3 with a size of 1*(m1*n1+m2*n2);

[0116] The one-dimensional vector P3 is split into a one-dimensional vector P4 with a size of 1*(m1*n1) and a one-dimensional vector P5 with a size of 1*(m2*n2);

[0117] (m2*n2);

[0118] Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P4 of size 1×

[0119] The one-dimensional vector P5 of (m2×n2) becomes an image img3 of size m1×n1 and an image img4 of size m2×n2, respectively.

[0120] S7, re-scrambling the two split images using an anti-DNA scanning scrambling algorithm to obtain encrypted images;

[0121] The rescrambling process includes:

[0122] C1. Assume the input image Img, obtain the length M and width N of the image Img, initialize the loop parameter I to 1, and initialize the loop parameter J to 1;

[0123] C2. Check I. If I is M / 2+1, go to step C8; otherwise, go to step C2.

[0124] C3. Check J. If J is 2N+1, increment the value of I by 1, set J to 1, and return to step C2. Otherwise, go to step C4.

[0125] C4. Select the operation to be performed based on the value of J, the vertical coordinate of the pixel value:

[0126]

[0127] C5. Select the operation to be performed based on the value of J. The horizontal coordinate of the pixel value:

[0128]

[0129] C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0, execute line(Num)=Img(X,1+NY) operation, where Num=Num+1, where Num represents the position number of the matrix line to be filled with pixels.

[0130] C7. Set the value of I to increment by 1, the value of J to increment by 1, and return to step C2;

[0131] C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

[0132] S8. When it is necessary to view two plaintext images collected on the IoT device, reverse processing is performed on the encrypted image to finally obtain two plaintext images.

[0133] The encrypted image is reverse processed, and the process includes:

[0134] The two encrypted images are inversely scrambled using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images.

[0135] Merge the two inverse scrambled images to obtain a merged image;

[0136] Combined with the initial parameters required for the obtained one-dimensional chaotic map, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion;

[0137] Split the image after inverse XOR diffusion to obtain two split images;

[0138] When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are reverse-scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

[0139] This embodiment adopts a dual-image scrambling-merging-diffusion-splitting-re-scrambling encryption method applicable to the Internet of Things, which has a good encryption effect and can greatly improve the security of the dual-image transmission process.

[0140] Specifically, the plaintext image is scrambled using the positive DNA scanning scrambling algorithm, and the split image is re-scrambled using the anti-DNA scanning scrambling algorithm. The combination of the two can significantly improve the scrambling effect and effectively resist attacks based on traditional scan line scrambling methods.

[0141] Specifically, using A chaotic mapping is performed, which is sensitive enough to the key, difficult to crack, and can generate a sufficiently random sequence.

[0142] In order to demonstrate the superiority and effectiveness of the method of the present invention, the following tests were performed:

[0143] 1. NIST test (if each indicator exceeds 0.01, it means the chaos test has passed), and the NIST test data of the chaotic map shown in Table 1 is obtained:

[0144] Test items P-value Passing situation Frequency test 0.085712 Successfully passed the test Within-block frequency test 0.623300 Successfully passed the test Accumulation and testing 0.515140 Successfully passed the test Run length test 0.283670 Successfully passed the test Longest run test 0.062820 Successfully passed the test Test of matrix rank 0.213300 Successfully passed the test Discrete Fourier Transform Test 0.233120 Successfully passed the test Non-overlapping template matching test 0.342115 Successfully passed the test Overlapping template matching test 0.494390 Successfully passed the test Common statistical tests 0.883160 Successfully passed the test Approximate entropy test 0.055670 Successfully passed the test Random walk test 0.543400 Successfully passed the test Random Walk Variable Test 0.549190 Successfully passed the test Continuity test 0.463260 Successfully passed the test Linear complexity test 0.893220 Successfully passed the test

[0145] Table 1

[0146] As can be seen from Table 1, the values ​​of each test of the present invention exceed 0.01, proving that the chaotic system adopted by the present invention can generate sufficiently random sequences.

[0147] 2. Encrypted visual testing:

[0148] Figure 2For the double-encrypted image obtained under the Internet of Things after the encrypted visualization test, the naked eye cannot identify the original image on the left from the image on the right, so the encryption algorithm adopted by the present invention has a good encryption effect.

[0149] 3. Correlation test, and obtain the test data shown in Table 2:

[0150]

[0151] Table 2

[0152] As can be seen from Table 2, the correlation test values ​​of the IoT image data in the horizontal, vertical, and diagonal directions after positive DNA scanning scrambling and reverse DNA scrambling are very low, which means that the correlation between adjacent pixels after image encryption is very low and statistical regularities are not easily detected. This shows that the method proposed in the present invention is generally very secure.

[0153] 4. Encryption speed test:

[0154] After experimental testing, the encryption algorithm proposed in the invention takes an average of 0.012 seconds to process a 512*512 image, so the encryption efficiency is very high and suitable for image protection in the Internet of Things.

[0155] 5. Change the parameters (change each parameter b by 10^-15, the initial value x0 by 10^-15, and N0 by 1), and perform key sensitivity analysis on the encrypted images with and without the parameters changed.

[0156] The data shown in Table 3:

[0157]

[0158] Table 3

[0159] As can be seen from Table 3, the NPCR and UACI values ​​returned by the test are both very high, which shows that the encryption algorithm proposed in the present invention is highly sensitive to the key and has sufficiently strong key sensitivity.

[0160] The embodiments described above are only preferred embodiments of the present invention and are not intended to limit the scope of implementation of the present invention. Therefore, any changes made based on the shape and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. An image protection method suitable for medical Internet of Things, characterized in that: It includes: Set a key; Use the key to obtain the initial parameters required for one-dimensional chaotic mapping, including the parameters and initial values of the chaotic mapping equation; Perform scrambling processing on two plaintext images collected on the Internet of Things device respectively to obtain two scrambled images; Merge the two scrambled images to obtain a merged image; Combine the initial parameters required for the obtained one-dimensional chaotic mapping, and perform XOR diffusion on the merged image to obtain an XOR-diffused image; Split the XOR-diffused image to obtain two split images; Perform re-scrambling processing on the two split images to obtain an encrypted image; When it is necessary to view the two plaintext images collected on the Internet of Things device, perform reverse processing on the encrypted image, and finally obtain two plaintext images; Both of the two plaintext images collected on the Internet of Things device are scrambled respectively through the positive DNA scanning scrambling algorithm. The scrambling process includes: A1. Input the plaintext image img, obtain the length m and width n of the plaintext image img, initialize the loop parameter i to 1, and initialize the loop parameter j to 1; A2. Check i. If i is m / 2 + 1, then execute step A8, otherwise execute step A2; A3. Check j. If j is 2n + 1, then set the value of i to increment by 1, set j to 1, and return to step A2, otherwise enter step A4; A4. Select the operation to be executed according to the value of j, the ordinate of the pixel value: A5. Select the operation to be executed according to the value of j, the abscissa of the pixel value: A6. Check the values of x and y. If both x ≤ m && y ≤ n && x > 0 && y > 0 are satisfied, then execute the operation Line(num) = img(x, y), num = num + 1, and num represents the position ordinal number of the matrix Line of the pixels to be filled; A7. Set the value of i to increment by 1, the value of j to increment by 1, and return to step A2; A8. Use the Reshpe formula to reshape the scanned matrix Line with a length of 1 and a width of m*n into a matrix Line with a length of m and a width of n.

2. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The merging of the two scrambled images includes: Convert the two images img1 and img2 into two one-dimensional vectors P1 and P2 respectively, where the size of img1 is m1×n1, the size of img2 is m2×n2, the size of P1 is 1×(m1×n1), and the size of P2 is 1×(m2×n2); Connect P1 and P2 end to end to form a one-dimensional vector P of 1×(m1×n1 + m2×n2).

3. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The XOR diffusion of the merged image includes: Suppose the size of the one-dimensional vector P obtained after merging the two scrambled images is 1×U, and its one-dimensional vector length L = 1×U; Generate a one-dimensional chaotic sequence key_stream with a length of L using the chaotic system, where 1 < i < N0 + L and i is an int-type variable, and N0 is a set value; Perform bitwise XOR on the one-dimensional vector P and the one-dimensional chaotic sequence key_stream to obtain the diffused sequence diffusion_img; Convert the ciphertext one-dimensional vector diffusion_img back into an image with a size of U.

4. The image protection method applicable to the medical Internet of Things according to claim 3, characterized in that: The process of generating a one-dimensional chaotic sequence key_stream includes: B1. Initialize variables: x(0)=x0, i=1; x0 is the initial value obtained using the key, specifically the starting point of chaos generation; B2. Iterate the one-dimensional chaotic map N0+L times to obtain an array sequence x of size 1*(N0+L) consisting of x(1)-x(N0+L). The specific steps of each iteration are divided into b1 and b2: b1: Update the value of x(i) using the chaotic mapping formula: Among them, b is the control parameter; b2: i = i + 1; B3. Take x(N0+1)-x(N0+L) as the new x array sequence and discard x(1)-x(N0); B4. Perform the following processing on the x sequence to convert it to the interval [0, 255] to obtain a one-dimensional chaotic sequence key_stream with a length of L; key_stream(i)=mod(floor(x(i)×10^12),256); i=1:L Among them, mod is the remainder operation, and floor is the rounding down operation.

5. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: Split the image after XOR diffusion, including: Convert the XOR-diffused image into a one-dimensional vector P3 of size 1×(m1×n1+m2×n2); Split the one-dimensional vector P3 into a one-dimensional vector P4 of size 1×(m1×n1) and a one-dimensional vector P5 of size 1×(m2×n2); Use the Reshpe formula to convert the one-dimensional vector P4 of size 1×(m1×n1) and the one-dimensional vector P5 of size 1×(m2×n2) into an image img3 of size m1×n1 and an image img4 of size m2×n2 respectively.

6. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The two split images are re-scrambled using the anti-DNA scanning scrambling algorithm. The re-scrambling process includes: C1. Assume the input image Img, obtain the length M and width N of the image Img, initialize the loop parameter I to 1, and initialize the loop parameter J to 1; C2. Check I. If I is M / 2+1, go to step C8; otherwise, go to step C2. C3. Check J. If J is 2N+1, increment the value of I by 1, set J to 1, and return to step C2. Otherwise, go to step C4. C4. Select the operation to be performed based on the value of J, the vertical coordinate of the pixel value: C5. Select the operation to be performed based on the value of J. The horizontal coordinate of the pixel value: C6. Check the values ​​of X and Y. If X≤M&&Y≤N&&X>0&&Y>0, execute line(Num)=Img(X,1+NY) operation, where Num=Num+1, where Num represents the position number of the matrix line to be filled with pixels. C7. Set the value of I to increment by 1, the value of J to increment by 1, and return to step C2; C8. Use the Reshpe formula to convert the scanned matrix line with a length of 1 and a width of M*N into a matrix line with a length of M and a width of N.

7. The image protection method applicable to the medical Internet of Things according to claim 1, characterized in that: The encrypted image is reverse processed, and the process includes: The two encrypted images are inversely scrambled using the anti-DNA scanning scrambling algorithm to obtain two inversely scrambled images. Merge the two inverse scrambled images to obtain a merged image; Combined with the initial parameters required for the obtained one-dimensional chaotic map, the combined image obtained in the previous step is subjected to inverse XOR diffusion to obtain an image after inverse XOR diffusion; Split the image after inverse XOR diffusion to obtain two split images; When it is necessary to view the two plaintext images collected on the IoT device, the split images obtained in the previous step are reverse-scrambled using the positive DNA scanning scrambling algorithm to obtain two plaintext images.

Images